Mypackage : Comprehensive Analyzing and Visualization of Gene Mutation, Expression
Pengmin Yang
2025-08-27
1 Introduction
用于NGS测序文件处理，突变相关分析和基因表达分析、可视化操作等
2 Generating MAF files
对于生信拉的基因突变数据文件，可以用mutToMAF函数转化成MAF文件。或者其它途径得到的MAF格式文件
If you’re using ANNOVAR for variant annotations, maftools has a handy function
annovarToMaffor converting tabular annovar outputs to MAF.
3 MAF field requirements
MAF files contain many fields ranging from chromosome names to cosmic annotations. However most of the analysis in maftools uses following fields.
Mandatory fields: Hugo_Symbol, Chromosome, Start_Position, End_Position, Reference_Allele, Tumor_Seq_Allele2, Variant_Classification, Variant_Type and Tumor_Sample_Barcode.
Recommended optional fields: non MAF specific fields containing VAF (Variant Allele Frequency) and amino acid change information.
Complete specification of MAF files can be found on NCI GDC documentation page.
This vignette demonstrates the usage and application of Mypackage on an example MAF file from Yunying cohort 1-5 and TCGA CRC cohort 6.
4 Installation
- 目前R包已经上传网络，有需要可以向作者索取安装包或者云安装，安装如下所示.
5 Overview of the package
Mypackage是集突变分析和基因表达分析于一体的综合性R包，包括MAF格式文件生成，通路突变差异分析，基因表达差异分析和单细胞分析以及相关的可视化.
5.1 Creat MAF files.
’mutToMAF’函数可以将生信拉取的NGS测序文件，例如gzy#6Q_g_639_review_for_report.xls，转换成MAF格式的文件
#example
data("clindata")#需要整合的样本数据
root_dir <-system.file("example", package = "Mypackage")#example数据
MAF <-mutToMAF(root_dir=root_dir,clin=clindata,saveDATA=FALSE,mut_filter=TRUE,
tumor_t=10,site_depth=100,hotspot_vaf=0.009,
non_hotspot_vaf=0.045,hotspotloss_vaf=0.095,non_hotspotloss_vaf=0.195)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, units## [31m Loop gzy#1007Q_g_639_review_for_report.xls time: 0.0501861572265625 s
## [0m
## [31m Loop gzy#101Q_g_639_review_for_report.xls time: 0.0321450233459473 s
## [0m
## [31m Loop gzy#1022Q_g_639_review_for_report.xls time: 0.0348548889160156 s
## [0m
## [31m Loop gzy#1028Q_g_639_review_for_report.xls time: 0.0384509563446045 s
## [0m
## [31m Loop gzy#1039Q_g_639_review_for_report.xls time: 0.0318408012390137 s
## [0m
## [31m Loop gzy#1049Q_g_639_review_for_report.xls time: 0.03857421875 s
## [0m
## [31m Loop gzy#1054Q_g_639_review_for_report.xls time: 0.0349640846252441 s
## [0m
## [31m Loop gzy#1060Q_g_639_review_for_report.xls time: 0.0409340858459473 s
## [0m
## [31m Loop gzy#1067Q_g_639_review_for_report.xls time: 0.0382578372955322 s
## [0m
## [31m Loop gzy#1072Q_g_639_review_for_report.xls time: 0.0408477783203125 s
## [0m
## [31m Loop gzy#1105Q_g_639_review_for_report.xls time: 0.0295600891113281 s
## [0m
## [31m Loop gzy#110Q_g_639_review_for_report.xls time: 0.032588005065918 s
## [0m
## [31m Loop gzy#1116Q_g_639_review_for_report.xls time: 0.0348560810089111 s
## [0m
## [31m Loop gzy#1117Q_g_639_review_for_report.xls time: 0.0526669025421143 s
## [0m
## [31m Loop gzy#1123Q_g_639_review_for_report.xls time: 0.0380508899688721 s
## [0m
## [31m Loop gzy#1128Q_g_639_review_for_report.xls time: 0.0453240871429443 s
## [0m
## [31m Loop gzy#1139Q_g_639_review_for_report.xls time: 0.0338740348815918 s
## [0m
## [31m Loop gzy#114Q_g_639_review_for_report.xls time: 0.0263550281524658 s
## [0m
## [31m Loop gzy#1153Q_g_639_review_for_report.xls time: 0.0348250865936279 s
## [0m
## [31m Loop gzy#1161Q_g_639_review_for_report.xls time: 0.0408530235290527 s
## [0m
## [31m Loop gzy#1171Q_g_639_review_for_report.xls time: 0.0442790985107422 s
## [0m
## [31m Loop gzy#1177Q_g_639_review_for_report.xls time: 0.0384631156921387 s
## [0m
## [31m Loop gzy#1184Q_g_639_review_for_report.xls time: 0.0327038764953613 s
## [0m
## [31m Loop gzy#1196Q_g_639_review_for_report.xls time: 0.0344798564910889 s
## [0m
## [31m Loop gzy#1197Q_g_639_review_for_report.xls time: 0.039280891418457 s
## [0m
## [31m Loop gzy#1199Q_g_639_review_for_report.xls time: 0.0367481708526611 s
## [0m
## [31m Loop gzy#1212Q_g_639_review_for_report.xls time: 0.0361571311950684 s
## [0m
## [31m Loop gzy#1221Q_g_639_review_for_report.xls time: 0.040241003036499 s
## [0m
## [31m Loop gzy#1243Q_g_639_review_for_report.xls time: 0.0416638851165771 s
## [0m
## [31m Loop gzy#1247Q_g_639_review_for_report.xls time: 0.038640022277832 s
## [0m
## [31m Loop gzy#1258Q_g_639_review_for_report.xls time: 0.0365221500396729 s
## [0m
## [31m Loop gzy#1280Q_g_639_review_for_report.xls time: 0.0260610580444336 s
## [0m
## [31m Loop gzy#1281Q_g_639_review_for_report.xls time: 0.0413579940795898 s
## [0m
## [31m Loop gzy#128Q_g_639_review_for_report.xls time: 0.0221409797668457 s
## [0m
## [31m Loop gzy#1296Q_g_639_review_for_report.xls time: 0.0277969837188721 s
## [0m
## [31m Loop gzy#129Q_g_639_review_for_report.xls time: 0.0263669490814209 s
## [0m
## [31m Loop gzy#1306Q_g_639_review_for_report.xls time: 0.0307548046112061 s
## [0m
## [31m Loop gzy#1331Q_g_639_review_for_report.xls time: 0.0383780002593994 s
## [0m
## [31m Loop gzy#1333Q_g_639_review_for_report.xls time: 0.0341758728027344 s
## [0m
## [31m Loop gzy#1341Q_g_639_review_for_report.xls time: 0.0340240001678467 s
## [0m
## [31m Loop gzy#1362Q_g_639_review_for_report.xls time: 0.0349102020263672 s
## [0m
## [31m Loop gzy#1363Q_g_639_review_for_report.xls time: 0.0349380970001221 s
## [0m
## [31m Loop gzy#1375Q_g_639_review_for_report.xls time: 0.0296170711517334 s
## [0m
## [31m Loop gzy#1384Q_g_639_review_for_report.xls time: 0.0336718559265137 s
## [0m
## [31m Loop gzy#1441Q_g_639_review_for_report.xls time: 0.0400218963623047 s
## [0m
## [31m Loop gzy#1468Q_g_639_review_for_report.xls time: 0.0426368713378906 s
## [0m
## [31m Loop gzy#1474Q_g_639_review_for_report.xls time: 0.0371661186218262 s
## [0m
## [31m Loop gzy#149Q_g_639_review_for_report.xls time: 0.0161919593811035 s
## [0m
## [31m Loop gzy#1502Q_g_639_review_for_report.xls time: 0.0454778671264648 s
## [0m
## [31m Loop gzy#154Q_g_639_review_for_report.xls time: 0.0188789367675781 s
## [0m
## [31m Loop gzy#1550Q_g_639_review_for_report.xls time: 0.0363280773162842 s
## [0m
## [31m Loop gzy#1579Q_g_639_review_for_report.xls time: 0.0423340797424316 s
## [0m
## [31m Loop gzy#1582Q_g_639_review_for_report.xls time: 0.0424480438232422 s
## [0m
## [31m Loop gzy#1586Q_g_639_review_for_report.xls time: 0.0304620265960693 s
## [0m
## [31m Loop gzy#1595Q_g_639_review_for_report.xls time: 0.0406959056854248 s
## [0m
## [31m Loop gzy#1611Q_g_639_review_for_report.xls time: 0.0349469184875488 s
## [0m
## [31m Loop gzy#1620Q_g_639_review_for_report.xls time: 0.0386369228363037 s
## [0m
## [31m Loop gzy#1632Q_g_639_review_for_report.xls time: 0.0412440299987793 s
## [0m
## [31m Loop gzy#1638Q_g_639_review_for_report.xls time: 0.038316011428833 s
## [0m
## [31m Loop gzy#1646Q_g_639_review_for_report.xls time: 0.036395788192749 s
## [0m
## [31m Loop gzy#1647Q_g_639_review_for_report.xls time: 0.0370559692382812 s
## [0m
## [31m Loop gzy#1656Q_g_639_review_for_report.xls time: 0.0474820137023926 s
## [0m
## [31m Loop gzy#1660Q_g_639_review_for_report.xls time: 0.0334079265594482 s
## [0m
## [31m Loop gzy#1666Q_g_639_review_for_report.xls time: 0.0550510883331299 s
## [0m
## [31m Loop gzy#1673Q_g_639_review_for_report.xls time: 0.0352799892425537 s
## [0m
## [31m Loop gzy#1683Q_g_639_review_for_report.xls time: 0.0245800018310547 s
## [0m
## [31m Loop gzy#1687Q_g_639_review_for_report.xls time: 0.0535769462585449 s
## [0m
## [31m Loop gzy#1688Q_g_639_review_for_report.xls time: 0.0336160659790039 s
## [0m
## [31m Loop gzy#16Q_g_639_review_for_report.xls time: 0.0208749771118164 s
## [0m
## [31m Loop gzy#1705Q_g_639_review_for_report.xls time: 0.0407700538635254 s
## [0m
## [31m Loop gzy#1707Q_g_639_review_for_report.xls time: 0.0365431308746338 s
## [0m
## [31m Loop gzy#170Q_g_639_review_for_report.xls time: 0.0210089683532715 s
## [0m
## [31m Loop gzy#1713Q_g_639_review_for_report.xls time: 0.0403051376342773 s
## [0m
## [31m Loop gzy#1726Q_g_639_review_for_report.xls time: 0.0393848419189453 s
## [0m
## [31m Loop gzy#173Q_g_639_review_for_report.xls time: 0.0219800472259521 s
## [0m
## [31m Loop gzy#1748Q_g_639_review_for_report.xls time: 0.0543339252471924 s
## [0m
## [31m Loop gzy#1749Q_g_639_review_for_report.xls time: 0.0439789295196533 s
## [0m
## [31m Loop gzy#175Q_g_639_review_for_report.xls time: 0.0255758762359619 s
## [0m
## [31m Loop gzy#1760Q_g_639_review_for_report.xls time: 0.0339739322662354 s
## [0m
## [31m Loop gzy#192Q_g_639_review_for_report.xls time: 0.0292568206787109 s
## [0m
## [31m Loop gzy#196Q_g_639_review_for_report.xls time: 0.0250132083892822 s
## [0m
## [31m Loop gzy#208Q_g_639_review_for_report.xls time: 0.0266220569610596 s
## [0m
## [31m Loop gzy#214Q_g_639_review_for_report.xls time: 0.0265560150146484 s
## [0m
## [31m Loop gzy#229Q_g_639_review_for_report.xls time: 0.0385329723358154 s
## [0m
## [31m Loop gzy#235Q_g_639_review_for_report.xls time: 0.0277061462402344 s
## [0m
## [31m Loop gzy#239Q_g_639_review_for_report.xls time: 0.0332720279693604 s
## [0m
## [31m Loop gzy#247Q_g_639_review_for_report.xls time: 0.0239999294281006 s
## [0m
## [31m Loop gzy#248Q_g_639_review_for_report.xls time: 0.035560131072998 s
## [0m
## [31m Loop gzy#255Q_g_639_review_for_report.xls time: 0.0225470066070557 s
## [0m
## [31m Loop gzy#263Q_g_639_review_for_report.xls time: 0.0195901393890381 s
## [0m
## [31m Loop gzy#265Q_g_639_review_for_report.xls time: 0.0339229106903076 s
## [0m
## [31m Loop gzy#272Q_g_639_review_for_report.xls time: 0.0229549407958984 s
## [0m
## [31m Loop gzy#277Q_g_639_review_for_report.xls time: 0.0295431613922119 s
## [0m
## [31m Loop gzy#280Q_g_639_review_for_report.xls time: 0.0249881744384766 s
## [0m
## [31m Loop gzy#281Q_g_639_review_for_report.xls time: 0.0264627933502197 s
## [0m
## [31m Loop gzy#284Q_g_639_review_for_report.xls time: 0.0471351146697998 s
## [0m
## [31m Loop gzy#313Q_g_639_review_for_report.xls time: 0.0315940380096436 s
## [0m
## [31m Loop gzy#328Q_g_639_review_for_report.xls time: 0.0438308715820312 s
## [0m
## [31m Loop gzy#32Q_g_639_review_for_report.xls time: 0.0304539203643799 s
## [0m
## [31m Loop gzy#338Q_g_639_review_for_report.xls time: 0.041295051574707 s
## [0m
## [31m Loop gzy#347Q_g_639_review_for_report.xls time: 0.0556631088256836 s
## [0m
## [31m Loop gzy#34Q_g_639_review_for_report.xls time: 0.0207479000091553 s
## [0m
## [31m Loop gzy#355Q_g_639_review_for_report.xls time: 0.0414969921112061 s
## [0m
## [31m Loop gzy#36Q_g_639_review_for_report.xls time: 0.0234990119934082 s
## [0m
## [31m Loop gzy#376Q_g_639_review_for_report.xls time: 0.0312221050262451 s
## [0m
## [31m Loop gzy#383Q_g_639_review_for_report.xls time: 0.0382370948791504 s
## [0m
## [31m Loop gzy#401Q_g_639_review_for_report.xls time: 0.0210621356964111 s
## [0m
## [31m Loop gzy#405Q_g_639_review_for_report.xls time: 0.0314369201660156 s
## [0m
## [31m Loop gzy#407Q_g_639_review_for_report.xls time: 0.0208220481872559 s
## [0m
## [31m Loop gzy#415Q_g_639_review_for_report.xls time: 0.0250520706176758 s
## [0m
## [31m Loop gzy#427Q_g_639_review_for_report.xls time: 0.0229480266571045 s
## [0m
## [31m Loop gzy#443Q_g_639_review_for_report.xls time: 0.0449631214141846 s
## [0m
## [31m Loop gzy#448Q_g_639_review_for_report.xls time: 0.0385940074920654 s
## [0m
## [31m Loop gzy#450Q_g_639_review_for_report.xls time: 0.0339028835296631 s
## [0m
## [31m Loop gzy#453Q_g_639_review_for_report.xls time: 0.030238151550293 s
## [0m
## [31m Loop gzy#455Q_g_639_review_for_report.xls time: 0.0485379695892334 s
## [0m
## [31m Loop gzy#456Q_g_639_review_for_report.xls time: 0.0336101055145264 s
## [0m
## [31m Loop gzy#458Q_g_639_review_for_report.xls time: 0.0259850025177002 s
## [0m
## [31m Loop gzy#46Q_g_639_review_for_report.xls time: 0.0226130485534668 s
## [0m
## [31m Loop gzy#471Q_g_639_review_for_report.xls time: 0.0276699066162109 s
## [0m
## [31m Loop gzy#476Q_g_639_review_for_report.xls time: 0.0299739837646484 s
## [0m
## [31m Loop gzy#477Q_g_639_review_for_report.xls time: 0.032494068145752 s
## [0m
## [31m Loop gzy#495Q_g_639_review_for_report.xls time: 0.0237941741943359 s
## [0m
## [31m Loop gzy#509Q_g_639_review_for_report.xls time: 0.0247330665588379 s
## [0m
## [31m Loop gzy#519Q_g_639_review_for_report.xls time: 0.0400588512420654 s
## [0m
## [31m Loop gzy#520Q_g_639_review_for_report.xls time: 0.0308849811553955 s
## [0m
## [31m Loop gzy#527Q_g_639_review_for_report.xls time: 0.0257048606872559 s
## [0m
## [31m Loop gzy#530Q_g_639_review_for_report.xls time: 0.0350780487060547 s
## [0m
## [31m Loop gzy#535Q_g_639_review_for_report.xls time: 0.0259649753570557 s
## [0m
## [31m Loop gzy#549Q_g_639_review_for_report.xls time: 0.0313842296600342 s
## [0m
## [31m Loop gzy#55Q_g_639_review_for_report.xls time: 0.031480073928833 s
## [0m
## [31m Loop gzy#568Q_g_639_review_for_report.xls time: 0.025629997253418 s
## [0m
## [31m Loop gzy#56Q_g_639_review_for_report.xls time: 0.0274791717529297 s
## [0m
## [31m Loop gzy#573Q_g_639_review_for_report.xls time: 0.0335450172424316 s
## [0m
## [31m Loop gzy#591Q_g_639_review_for_report.xls time: 0.0355699062347412 s
## [0m
## [31m Loop gzy#59Q_g_639_review_for_report.xls time: 0.0285429954528809 s
## [0m
## [31m Loop gzy#601Q_g_639_review_for_report.xls time: 0.0485389232635498 s
## [0m
## [31m Loop gzy#606Q_g_639_review_for_report.xls time: 0.0366849899291992 s
## [0m
## [31m Loop gzy#619Q_g_639_review_for_report.xls time: 0.0424301624298096 s
## [0m
## [31m Loop gzy#620Q_g_639_review_for_report.xls time: 0.0484368801116943 s
## [0m
## [31m Loop gzy#632Q_g_639_review_for_report.xls time: 0.0355730056762695 s
## [0m
## [31m Loop gzy#638Q_g_639_review_for_report.xls time: 0.0410878658294678 s
## [0m
## [31m Loop gzy#653Q_g_639_review_for_report.xls time: 0.037074089050293 s
## [0m
## [31m Loop gzy#669Q_g_639_review_for_report.xls time: 0.0326588153839111 s
## [0m
## [31m Loop gzy#688Q_g_639_review_for_report.xls time: 0.103429079055786 s
## [0m
## [31m Loop gzy#699Q_g_639_review_for_report.xls time: 0.031588077545166 s
## [0m
## [31m Loop gzy#6Q_g_639_review_for_report.xls time: 0.019556999206543 s
## [0m
## [31m Loop gzy#703Q_g_639_review_for_report.xls time: 0.0411009788513184 s
## [0m
## [31m Loop gzy#714Q_g_639_review_for_report.xls time: 0.028425931930542 s
## [0m
## [31m Loop gzy#718Q_g_639_review_for_report.xls time: 0.0385169982910156 s
## [0m
## [31m Loop gzy#71Q_g_639_review_for_report.xls time: 0.0268590450286865 s
## [0m
## [31m Loop gzy#722Q_g_639_review_for_report.xls time: 0.0295989513397217 s
## [0m
## [31m Loop gzy#735Q_g_639_review_for_report.xls time: 0.0278708934783936 s
## [0m
## [31m Loop gzy#743Q_g_639_review_for_report.xls time: 0.0279049873352051 s
## [0m
## [31m Loop gzy#744Q_g_639_review_for_report.xls time: 0.0355300903320312 s
## [0m
## [31m Loop gzy#748Q_g_639_review_for_report.xls time: 0.0363929271697998 s
## [0m
## [31m Loop gzy#749Q_g_639_review_for_report.xls time: 0.035783052444458 s
## [0m
## [31m Loop gzy#756Q_g_639_review_for_report.xls time: 0.0251269340515137 s
## [0m
## [31m Loop gzy#758A1Q_g_639_review_for_report.xls time: 0.0318140983581543 s
## [0m
## [31m Loop gzy#772Q_g_639_review_for_report.xls time: 0.0423810482025146 s
## [0m
## [31m Loop gzy#78Q_g_639_review_for_report.xls time: 0.0186538696289062 s
## [0m
## [31m Loop gzy#790Q_g_639_review_for_report.xls time: 0.0318808555603027 s
## [0m
## [31m Loop gzy#795Q_g_639_review_for_report.xls time: 0.0245118141174316 s
## [0m
## [31m Loop gzy#800Q_g_639_review_for_report.xls time: 0.0286071300506592 s
## [0m
## [31m Loop gzy#811Q_g_639_review_for_report.xls time: 0.0278759002685547 s
## [0m
## [31m Loop gzy#82Q_g_639_review_for_report.xls time: 0.020380973815918 s
## [0m
## [31m Loop gzy#831Q_g_639_review_for_report.xls time: 0.0365421772003174 s
## [0m
## [31m Loop gzy#832Q_g_639_review_for_report.xls time: 0.0337600708007812 s
## [0m
## [31m Loop gzy#838Q_g_639_review_for_report.xls time: 0.0242860317230225 s
## [0m
## [31m Loop gzy#839Q_g_639_review_for_report.xls time: 0.0288069248199463 s
## [0m
## [31m Loop gzy#843Q_g_639_review_for_report.xls time: 0.0302529335021973 s
## [0m
## [31m Loop gzy#844Q_g_639_review_for_report.xls time: 0.0230169296264648 s
## [0m
## [31m Loop gzy#848Q_g_639_review_for_report.xls time: 0.0297150611877441 s
## [0m
## [31m Loop gzy#84Q_g_639_review_for_report.xls time: 0.032397985458374 s
## [0m
## [31m Loop gzy#858Q_g_639_review_for_report.xls time: 0.0325701236724854 s
## [0m
## [31m Loop gzy#85Q_g_639_review_for_report.xls time: 0.0421891212463379 s
## [0m
## [31m Loop gzy#865Q_g_639_review_for_report.xls time: 0.0314300060272217 s
## [0m
## [31m Loop gzy#869Q_g_639_review_for_report.xls time: 0.0313520431518555 s
## [0m
## [31m Loop gzy#890Q_g_639_review_for_report.xls time: 0.0269908905029297 s
## [0m
## [31m Loop gzy#893Q_g_639_review_for_report.xls time: 0.0329298973083496 s
## [0m
## [31m Loop gzy#895Q_g_639_review_for_report.xls time: 0.027446985244751 s
## [0m
## [31m Loop gzy#89Q_g_639_review_for_report.xls time: 0.0206270217895508 s
## [0m
## [31m Loop gzy#902Q_g_639_review_for_report.xls time: 0.0285041332244873 s
## [0m
## [31m Loop gzy#923Q_g_639_review_for_report.xls time: 0.0380890369415283 s
## [0m
## [31m Loop gzy#933Q_g_639_review_for_report.xls time: 0.0325620174407959 s
## [0m
## [31m Loop gzy#945Q_g_639_review_for_report.xls time: 0.0309188365936279 s
## [0m
## [31m Loop gzy#961Q_g_639_review_for_report.xls time: 0.0289218425750732 s
## [0m
## [31m Loop gzy#973Q_g_639_review_for_report.xls time: 0.0303330421447754 s
## [0m
## [31m Loop gzy#986Q_g_639_review_for_report.xls time: 0.0424778461456299 s
## [0m
## Total: 189## Druggable Report_C2 Tumor_VAF Chromosome Start_Position
## 1 exon5,c.1227C>T,p.G409= 0.160 4 66356270
## 2 - exon11,c.1401_1404dup,p.L469* 0.193 5 112157674
## 3 exon16,c.3080A>G,p.Y1027C 0.112 5 112174371
## 4 - exon16,c.4348C>T,p.R1450* 0.113 5 112175639
## 5 exon8,c.797G>T,p.G266V 0.235 17 7577141
## 6 1 exon10,c.1633G>A,p.E545K 0.189 3 178936091
## End_Position Reference_Allele Tumor_Seq_Allele2 FILTER Hugo_Symbol
## 1 66356270 G A PASS EPHA5
## 2 112157674 C CAATG PASS APC
## 3 112174371 A G PASS APC
## 4 112175639 C T PASS APC
## 5 7577141 C A PASS TP53
## 6 178936095 G A clustered_events PIK3CA
## RefSeq HGVSc HGVSp t_ref_count t_alt_count depth
## 1 NM_004439.8 c.1227C>T p.Gly409= 386 77 463
## 2 NM_000038.6 c.1401_1404dup p.Leu469* 2099 499 2598
## 3 NM_000038.6 c.3080A>G p.Tyr1027Cys 1955 237 2192
## 4 NM_000038.6 c.4348C>T p.Arg1450* 1680 219 1899
## 5 NM_000546.6 c.797G>T p.Gly266Val 4247 1296 5543
## 6 NM_006218.4 c.1633G>A p.Glu545Lys 2904 692 3596
## file_name Tumor_Sample_Barcode is_hotsgene
## 1 gzy#1007Q_g_639_review_for_report.xls #1007 FALSE
## 2 gzy#1007Q_g_639_review_for_report.xls #1007 FALSE
## 3 gzy#1007Q_g_639_review_for_report.xls #1007 FALSE
## 4 gzy#1007Q_g_639_review_for_report.xls #1007 FALSE
## 5 gzy#1007Q_g_639_review_for_report.xls #1007 FALSE
## 6 gzy#101Q_g_639_review_for_report.xls #101 FALSE
## mutation_class HGVSp_Short Variant_Type Variant_Classification
## 1 4.0 p.G409= SNP Silent
## 2 2.0 p.L469* INS Frame_Shift_Ins
## 3 4.0 p.Y1027C SNP Missense_Mutation
## 4 2.0 p.R1450* SNP Nonsense_Mutation
## 5 4.0 p.G266V SNP Missense_Mutation
## 6 1.1 p.E545K SNP Missense_Mutation5.1.1 MAF Visualization
5.1.1.1 mut_network
Gene Mutation Co_Occurence/Mutually_Exclusive analysis and visualization
##
## Attaching package: 'igraph'## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## union## Loading required package: ggplot2##
## Attaching package: 'tidyr'## The following object is masked from 'package:igraph':
##
## crossing## -Validating
## --Removed 35515 duplicated variants
## -Silent variants: 68601
## -Summarizing
## --Possible FLAGS among top ten genes:
## TTN
## SYNE1
## MUC16
## -Processing clinical data
## --Missing clinical data
## -Finished in 5.650s elapsed (5.610s cpu)
## $network
## gene1 gene2 pValue oddsRatio 00 01 11 10 pAdj
## <char> <char> <num> <num> <int> <int> <int> <int> <num>
## 1: ZFHX4 FAT4 2.025755e-18 7.9426605 415 67 58 45 3.683192e-17
## 2: PCLO OBSCN 1.867129e-15 8.1230346 444 57 43 41 3.111882e-14
## 3: RYR3 OBSCN 4.627314e-15 8.0108583 445 58 42 40 7.118944e-14
## 4: RYR3 CSMD3 9.368810e-15 7.9370909 447 56 41 41 1.319105e-13
## 5: RYR3 PCLO 9.893290e-15 8.5288571 457 46 38 44 1.319105e-13
## ---
## 186: TP53 TTN 8.660303e-01 0.9679041 130 109 155 191 8.837044e-01
## 187: TP53 FAT3 9.041778e-01 0.9712145 205 34 48 298 9.179470e-01
## 188: LRP1B TP53 1.000000e+00 0.9812539 199 289 57 40 1.000000e+00
## 189: LRP1B KRAS 1.000000e+00 1.0022500 282 206 41 56 1.000000e+00
## 190: DNAH11 KRAS 1.000000e+00 1.0148352 288 210 37 50 1.000000e+00
## Event pair event_ratio
## <char> <char> <char>
## 1: Co_Occurence FAT4, ZFHX4 58/112
## 2: Co_Occurence OBSCN, PCLO 43/98
## 3: Co_Occurence OBSCN, RYR3 42/98
## 4: Co_Occurence CSMD3, RYR3 41/97
## 5: Co_Occurence PCLO, RYR3 38/90
## ---
## 186: Mutually_Exclusive TP53, TTN 155/300
## 187: Mutually_Exclusive FAT3, TP53 48/332
## 188: Mutually_Exclusive LRP1B, TP53 57/329
## 189: Co_Occurence KRAS, LRP1B 41/262
## 190: Co_Occurence DNAH11, KRAS 37/260
##
## $plot## Warning: The following aesthetics were dropped during statistical transformation: xend
## and yend.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## Warning: Raster pixels are placed at uneven horizontal intervals and will be shifted
## ℹ Consider using `geom_tile()` instead.
## Raster pixels are placed at uneven horizontal intervals and will be shifted
## ℹ Consider using `geom_tile()` instead.
5.1.1.2 maf_cor
This function analyzes mutation and clinical data correlation with MAF format data. It calculates the VAF (Variant Allele Frequency) for each gene and selects the top genes based on the specified criteria. Then, it calculates the correlation between the selected genes and a specified clinical variable. The function also generates a correlation plot to visualize the relationships between the genes and the clinical variable.
## corrplot 0.95 loaded#example
data<-maf_cor(mutation_data=MAF,
clin=NULL,
gene=NULL,top=20,
cin_col=NULL,corrplot_method=c("pie"))## Time difference of 0.04077601 secs
## Registered S3 methods overwritten by 'ggcor':
## method from
## &.gg patchwork
## *.gg patchwork##
## ********************************************************## Note: As of version 0.9.8, ggcor does not change the## default ggplot2 continuous fill scale anymore. To## recover the previous behavior, execute:## set_scale()## Instead of using the set_scale() function, we## recommend adding the 'scale_fill_*()' function## to the plot as needed.## ********************************************************##
## Attaching package: 'ggcor'## The following object is masked from 'package:stats':
##
## filter
## TP53 APC KRAS PIK3CA LRP1B RET FBXW7 SMAD4 ATM FAT1 KMT2C ERBB2 ARID1A
## #1007 1 3 0 0 0 0 0 0 0 0 0 0 0
## #101 1 2 1 1 0 0 1 0 0 0 0 0 0
## #1022 1 1 0 0 1 0 0 0 0 1 0 0 0
## #1028 1 2 0 0 0 0 0 0 0 0 0 0 0
## #1039 0 1 1 2 0 0 1 0 0 0 0 0 0
## #1049 1 0 1 1 0 0 0 0 0 0 0 0 0
## ERBB4 EPHA5 BRAF BRCA2 CHD4 PRKDC AMER1
## #1007 0 1 0 0 0 0 0
## #101 0 0 0 0 0 0 0
## #1022 0 0 0 1 0 0 0
## #1028 0 1 0 0 0 0 0
## #1039 0 0 0 0 0 0 0
## #1049 0 0 0 0 0 0 05.1.1.3 path_mut_visual
This function visualizes the mutation rate of tumor pathways based on SNV and gene data.
#example
data("mutation_CRC")
data("gene_group_data")
data("pathway_data")
gene_of_interest<-colnames(gene_group_data)[[2]]
tumor_type="CRC TCGA"
color_vector=c("#757575", "#FF4040")
result <- path_mut_visual(result_data=NULL,
SNV = mutation_CRC,
gene = gene_of_interest,
Gene_group = gene_group_data,
Type = c("Wild", "Mut"),
pathway_gene_data = pathway_data,
tumor = tumor_type,
heatmap=TRUE,
heatmap_col=NULL,
color = color_vector,
test = "wilcox.test",ns=FALSE,
p_0.05=FALSE,p_0.01=FALSE,p_0.001=FALSE,p_0.0001=FALSE)## Warning: package 'ggpubr' was built under R version 4.5.1## Warning: package 'ComplexHeatmap' was built under R version 4.5.1## Warning: package 'tidyHeatmap' was built under R version 4.5.1## [31m 1 : Oh yeah! Apoptosis 有组间显著性差异(p<0.0001) [0m
## [31m 2 : Oh yeah! Cell cycle 有组间显著性差异(p<0.0001) [0m
## [31m 3 : Oh yeah! Chromatin histone modifiers 有组间显著性差异(p<0.0001) [0m
## [31m 4 : Oh yeah! Chromatin other 有组间显著性差异(p<0.0001) [0m
## [31m 5 : Oh yeah! Chromatin SWI/SNF complex 有组间显著性差异(p<0.0001) [0m
## [31m 6 : Oh yeah! Epigenetics DNA modifiers 有组间显著性差异(p<0.0001) [0m
## [31m 7 : Oh yeah! Genome integrity 有组间显著性差异(p<0.0001) [0m
## [31m 8 : Oh yeah! Histone modification 有组间显著性差异(p<0.0001) [0m
## [31m 9 : Oh yeah! Immune signaling 有组间显著性差异(p<0.0001) [0m
## [31m 10 : Oh yeah! MAPK signaling 有组间显著性差异(p<0.0001) [0m
## [31m 11 : Oh yeah! Metabolism 有组间显著性差异(p<0.0001) [0m
## [31m 12 : Oh yeah! NFKB signaling 有组间显著性差异(p<0.0001) [0m
## [31m 13 : Oh yeah! NOTCH signaling 有组间显著性差异(p<0.0001) [0m
## [31m 14 : Oh yeah! Other 有组间显著性差异(p<0.0001) [0m
## [31m 15 : Oh yeah! Other signaling 有组间显著性差异(p<0.0001) [0m
## [31m 16 : Oh yeah! PI3K signaling 有组间显著性差异(p<0.0001) [0m
## [31m 17 : Oh yeah! Protein homeostasis/ubiquitination 有组间显著性差异(p<0.0001) [0m
## [31m 18 : Oh yeah! RNA abundance 有组间显著性差异(p<0.0001) [0m
## [31m 19 : Oh yeah! RTK signaling 有组间显著性差异(p<0.0001) [0m
## [31m 20 : Oh yeah! Splicing 有组间显著性差异(p<0.0001) [0m
## [31m 21 : Oh yeah! TGFB signaling 有组间显著性差异(p<0.0001) [0m
## [31m 22 : Oh yeah! TOR signaling 有组间显著性差异(p<0.0001) [0m
## [31m 23 : Oh yeah! Transcription factor 有组间显著性差异(p<0.0001) [0m
## [31m 24 : Oh yeah! Wnt/B-catenin signaling 有组间显著性差异(p<0.0001) [0m## Warning: Vectorized input to `element_text()` is not officially supported.
## ℹ Results may be unexpected or may change in future versions of ggplot2.
5.2 基因表达数据.
5.2.1 Epression data处理
exp_geneIDtoSYMBOL
This function convert the gene name of expression data to SYMBOL.
## ## 'select()' returned 1:1 mapping between keys and columns## Warning in clusterProfiler::bitr(exp[[colnames(exp)[[1]]]], fromType =
## "ENTREZID", : 0.01% of input gene IDs are fail to map...## ENTREZID SYMBOL
## 1 1 A1BG
## 2 503538 A1BG-AS1
## 3 29974 A1CF
## 4 2 A2M
## 5 144571 A2M-AS1
## 6 144568 A2ML1
## 7 100874108 A2ML1-AS1
## 8 106478979 A2ML1-AS2
## 9 3 A2MP1
## 10 127550 A3GALT2## ENTREZID SYMBOL TCGA-3L-AA1B-01A TCGA-4N-A93T-01A TCGA-4T-AA8H-01A
## 1 1 A1BG -0.2986 -0.1453 -0.6105
## 2 10 NAT2 1.1236 0.2581 1.8891
## 3 100 ADA -0.6428 -0.6827 -2.4357
## 4 1000 CDH2 0.7466 -0.6900 -0.9559
## 5 10000 AKT3 0.4759 -1.4576 -1.4286
## 6 100009613 LINC02584 -0.2082 -0.9571 -0.9571
## 7 100009667 POU5F1P5 -0.6915 -0.6915 -0.3571
## 8 100009668 POU5F1P6 0.8188 -0.5041 -0.6792
## 9 100009676 ZBTB11-AS1 0.4024 -0.8768 -0.1029
## 10 10001 MED6 0.3399 -1.4712 -0.73395.2.2 Epression Visualization
5.2.2.1 immu_visual
This function visualizes the mutation rate of immune score based on provided immune score or gene expression data.
#example
data("Gene_group_CRC1")
exp_CRC<-exp_raw$data
result<-immu_visual(im=NULL,exp=exp_CRC[,-1],
method = 'epic',
sample_group=Gene_group_CRC1,
tumor="CRC TCGA",heatmap=TRUE,
Type=c("Wild", "Mut"),
color=c("#757575", "#FF4040"),
geom_text=TRUE,
test = "wilcox.test")## Loading required package: tibble##
## Attaching package: 'tibble'## The following object is masked from 'package:igraph':
##
## as_data_frame## Loading required package: survival## Loading required package: patchwork## Warning: package 'patchwork' was built under R version 4.5.1## Loading required package: survminer##
## Attaching package: 'survminer'## The following object is masked from 'package:survival':
##
## myeloma## ==========================================================================
## IOBR v0.99.8 Immuno-Oncology Biological Research
## For Tutorial: https://iobr.github.io/book/
## For Help: https://github.com/IOBR/IOBR/issues
##
## If you use IOBR in published research, please cite:
## DQ Zeng, YR Fang, WJ Qiu, ..., GC Yu*, WJ Liao*, (2024)
## IOBR2: Multidimensional Decoding Tumor Microenvironment for Immuno-Oncology Research.
## bioRxiv, 2024.01.13.575484;
## https://www.biorxiv.org/content/10.1101/2024.01.13.575484v2.full.pdf
## Higly Cited Paper and Hot Paper of WOS
## ==========================================================================##
## >>> Running EPIC## Warning in IOBR::EPIC(bulk = eset, reference = ref, mRNA_cell = NULL, scaleExprs = TRUE): The optimization didn't fully converge for some samples:
## TCGA-A6-2680-01A; TCGA-A6-2681-01A; TCGA-A6-2683-01A; TCGA-A6-4107-01A; TCGA-A6-5661-01A; TCGA-A6-6782-01A; TCGA-AA-3488-01A; TCGA-AA-3511-01A; TCGA-AA-3516-01A; TCGA-AA-3526-01A; TCGA-AA-3529-01A; TCGA-AA-3530-01A; TCGA-AA-3548-01A; TCGA-AA-3560-01A; TCGA-AA-3562-01A; TCGA-AA-3684-01A; TCGA-AA-3692-01A; TCGA-AA-3697-01A; TCGA-AA-3712-01A; TCGA-AA-3815-01A; TCGA-AA-3821-01A; TCGA-AA-3831-01A; TCGA-AA-3837-01A; TCGA-AA-3850-01A; TCGA-AA-3862-01A; TCGA-AA-3977-01A; TCGA-AA-A00E-01A; TCGA-AA-A00J-01A; TCGA-AA-A00Z-01A; TCGA-AA-A010-01A; TCGA-AA-A01D-01A; TCGA-AA-A01R-01A; TCGA-AA-A02H-01A; TCGA-AA-A02R-01A; TCGA-AF-6655-01A; TCGA-AG-3578-01A; TCGA-AG-3584-01A; TCGA-AG-3599-01A; TCGA-AG-3608-01A; TCGA-AG-3727-01A; TCGA-AG-3887-01A; TCGA-AG-3896-01A; TCGA-AG-A00H-01A; TCGA-AG-A00Y-01A; TCGA-AG-A014-01A; TCGA-AG-A01J-01A; TCGA-AG-A02N-01A; TCGA-AH-6549-01A; TCGA-AZ-4308-01A; TCGA-AZ-4615-01A; TCGA-AZ-6598-01A; TCGA-AZ-6601-01A; TCGA-AZ-6603-01A; TCGA-AZ-6607-01A; TCGA-CA-5796-01A; TCGA-CK-4948-01B; TCGA-CK-4950-01A; TCGA-CK-5915-01A; TCGA-CM-4750-01A; TCGA-CM-5861-01A; TCGA-CM-6169-01A; TCGA-CM-6170-01A; TCGA-CM-6676-01A; TCGA-D5-5537-01A; TCGA-D5-6532-01A; TCGA-D5-6533-01A; TCGA-D5-6535-01A; TCGA-D5-6536-01A; TCGA-D5-6537-01A; TCGA-D5-6923-01A; TCGA-D5-6928-01A; TCGA-D5-6929-01A; TCGA-DC-6158-01A; TCGA-DC-6682-01A; TCGA-DM-A1D7-01A; TCGA-DM-A28H-01A; TCGA-DY-A0XA-01A; TCGA-EI-6512-01A; TCGA-EI-6513-01A; TCGA-EI-6883-01A; TCGA-EI-7004-01A; TCGA-F4-6570-01A; TCGA-F4-6807-01A; TCGA-F4-6808-01A; TCGA-F5-6465-01A; TCGA-F5-6810-01A; TCGA-F5-6861-01A; TCGA-F5-6864-01A; TCGA-G4-6293-01A; TCGA-G4-6297-01A; TCGA-G4-6302-01A; TCGA-G4-6310-01A; TCGA-G4-6314-01A; TCGA-G4-6321-01A; TCGA-G4-6322-01A; TCGA-G4-6588-01A; TCGA-G5-6233-01A; TCGA-QG-A5YW-01A; TCGA-WS-AB45-01A
## - check fit.gof for the convergeCode and convergeMessage## Warning in IOBR::EPIC(bulk = eset, reference = ref, mRNA_cell = NULL,
## scaleExprs = TRUE): mRNA_cell value unknown for some cell types: CAFs,
## Endothelial - using the default value of 0.4 for these but this might bias the
## true cell proportions from all cell types.## # A tibble: 4,569 × 9
## # Groups: method, Group [16]
## SAMPLE_ID Group method method_score Q1 Q3 IQR LowerLimit
## <chr> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCGA-3L-AA1B-01A Wild CAFs 1.24e- 8 1.87e-6 0.0171 0.0171 -0.0256
## 2 TCGA-3L-AA1B-01A Wild CD4 T… 2.37e- 1 1.40e-7 0.142 0.142 -0.214
## 3 TCGA-3L-AA1B-01A Wild CD8 T… 4.62e- 3 2.31e-7 0.114 0.114 -0.171
## 4 TCGA-3L-AA1B-01A Wild Endot… 3.04e- 1 1.05e-6 0.159 0.159 -0.239
## 5 TCGA-3L-AA1B-01A Wild Macro… 7.11e-10 1.32e-7 0.0118 0.0118 -0.0177
## 6 TCGA-3L-AA1B-01A Wild NKcel… 1.94e- 8 9.59e-9 0.00460 0.00460 -0.00690
## 7 TCGA-3L-AA1B-01A Wild other… 1.47e- 5 3.30e-1 0.920 0.590 -0.555
## 8 TCGA-4N-A93T-01A Wild Bcells 7.51e- 9 1.27e-7 0.0691 0.0691 -0.104
## 9 TCGA-4N-A93T-01A Wild CAFs 2.42e- 2 1.87e-6 0.0171 0.0171 -0.0256
## 10 TCGA-4N-A93T-01A Wild CD4 T… 1.14e- 1 1.40e-7 0.142 0.142 -0.214
## # ℹ 4,559 more rows
## # ℹ 1 more variable: UpperLimit <dbl>
## [32m 1 : Oopps! Bcells 没有组间显著性差异 [0m
## [32m 2 : Oopps! CAFs 没有组间显著性差异 [0m
## [32m 3 : Oopps! CD4 Tcells 没有组间显著性差异 [0m
## [32m 4 : Oopps! CD8 Tcells 没有组间显著性差异 [0m
## [32m 5 : Oopps! Endothelial 没有组间显著性差异 [0m
## [32m 6 : Oopps! Macrophages 没有组间显著性差异 [0m
## [32m 7 : Oopps! NKcells 没有组间显著性差异 [0m
## [32m 8 : Oopps! otherCells 没有组间显著性差异 [0m## Warning: Vectorized input to `element_text()` is not officially supported.
## ℹ Results may be unexpected or may change in future versions of ggplot2.## Time difference of 22.01418 secs
#当method为xCell,estimate和cibersort时,可以用以下方式增强可视化的可读性
result_imm01<-immu_visual(im=NULL,exp=exp_CRC[,-1],#
method = 'xCell',
sample_group=Gene_group_CRC1,
tumor="CRC TCGA",heatmap=TRUE,
Type=c("Wild", "Mut"),
color=c("#757575", "#FF4040"),
geom_text=TRUE,
test = "wilcox.test")## [1] "Num. of genes: 10426"## ℹ GSVA version 2.3.1## ! No annotation metadata available in the input expression data object## ! Attempting to directly match identifiers in expression data to gene sets## ℹ Calculating ssGSEA scores for 489 gene sets## ℹ Calculating ranks## ℹ Calculating rank weights## Calculating ssGSEA scores ■■■■ 11% | ETA: 10s## Calculating ssGSEA scores ■■■■■■■■■■■ 32% | ETA: 5s## Calculating ssGSEA scores ■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 89% | ETA: 1s## Calculating ssGSEA scores ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 100% | ETA: 0s## ✔ Calculations finished## # A tibble: 37,958 × 9
## # Groups: method, Group [128]
## SAMPLE_ID Group method method_score Q1 Q3 IQR LowerLimit
## <chr> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCGA-3L-AA1B… Wild Adipo… 3.00e-19 2.20e-21 3.54e- 2 3.54e- 2 -5.31e- 2
## 2 TCGA-3L-AA1B… Wild Astro… 4.59e- 3 0 2.29e- 1 2.29e- 1 -3.43e- 1
## 3 TCGA-3L-AA1B… Wild B-cel… 8.24e- 1 6.59e-18 4.13e- 1 4.13e- 1 -6.19e- 1
## 4 TCGA-3L-AA1B… Wild Basop… 2.03e-16 5.38e-17 3.75e- 1 3.75e- 1 -5.63e- 1
## 5 TCGA-3L-AA1B… Wild CD4+ … 4.93e- 2 2.47e-18 1.96e- 1 1.96e- 1 -2.93e- 1
## 6 TCGA-3L-AA1B… Wild CD4+ … 2.30e- 1 1.14e- 1 5.86e- 1 4.72e- 1 -5.94e- 1
## 7 TCGA-3L-AA1B… Wild CD8+ … 1.20e- 1 3.19e- 2 3.29e- 1 2.97e- 1 -4.14e- 1
## 8 TCGA-3L-AA1B… Wild CD8+ … 1.31e-17 6.24e-20 2.66e- 1 2.66e- 1 -3.98e- 1
## 9 TCGA-3L-AA1B… Wild CD8+ … 3.54e-17 0 2.90e-17 2.90e-17 -4.35e-17
## 10 TCGA-3L-AA1B… Wild CD8+ … 0 7.00e-18 1.12e- 1 1.12e- 1 -1.68e- 1
## # ℹ 37,948 more rows
## # ℹ 1 more variable: UpperLimit <dbl>
## [31m 1 : Oh yeah! Adipocytes 有组间显著性差异(p<0.05) [0m
## [32m 2 : Oopps! Astrocytes 没有组间显著性差异 [0m
## [31m 3 : Oh yeah! B-cells 有组间显著性差异(p<0.05) [0m
## [31m 4 : Oh yeah! Basophils 有组间显著性差异(p<0.01) [0m
## [32m 5 : Oopps! CD4+ T-cells 没有组间显著性差异 [0m
## [32m 6 : Oopps! CD4+ Tcm 没有组间显著性差异 [0m
## [32m 7 : Oopps! CD4+ Tem 没有组间显著性差异 [0m
## [31m 8 : Oh yeah! CD4+ memory T-cells 有组间显著性差异(p<0.01) [0m
## [32m 9 : Oopps! CD4+ naive T-cells 没有组间显著性差异 [0m
## [31m 10 : Oh yeah! CD8+ T-cells 有组间显著性差异(p<0.001) [0m
## [31m 11 : Oh yeah! CD8+ Tcm 有组间显著性差异(p<0.0001) [0m
## [31m 12 : Oh yeah! CD8+ Tem 有组间显著性差异(p<0.0001) [0m
## [32m 13 : Oopps! CD8+ naive T-cells 没有组间显著性差异 [0m
## [32m 14 : Oopps! CLP 没有组间显著性差异 [0m
## [32m 15 : Oopps! CMP 没有组间显著性差异 [0m
## [32m 16 : Oopps! Chondrocytes 没有组间显著性差异 [0m
## [31m 17 : Oh yeah! Class-switched memory B-cells 有组间显著性差异(p<0.05) [0m
## [32m 18 : Oopps! DC 没有组间显著性差异 [0m
## [32m 19 : Oopps! Endothelial cells 没有组间显著性差异 [0m
## [31m 20 : Oh yeah! Eosinophils 有组间显著性差异(p<0.05) [0m
## [32m 21 : Oopps! Epithelial cells 没有组间显著性差异 [0m
## [31m 22 : Oh yeah! Erythrocytes 有组间显著性差异(p<0.05) [0m
## [31m 23 : Oh yeah! Fibroblasts 有组间显著性差异(p<0.01) [0m
## [31m 24 : Oh yeah! GMP 有组间显著性差异(p<0.05) [0m
## [31m 25 : Oh yeah! HSC 有组间显著性差异(p<0.01) [0m
## [31m 26 : Oh yeah! Hepatocytes 有组间显著性差异(p<0.01) [0m
## [32m 27 : Oopps! Keratinocytes 没有组间显著性差异 [0m
## [31m 28 : Oh yeah! MEP 有组间显著性差异(p<0.01) [0m
## [32m 29 : Oopps! MPP 没有组间显著性差异 [0m
## [32m 30 : Oopps! MSC 没有组间显著性差异 [0m
## [31m 31 : Oh yeah! Macrophages 有组间显著性差异(p<0.01) [0m
## [31m 32 : Oh yeah! Macrophages M1 有组间显著性差异(p<0.001) [0m
## [31m 33 : Oh yeah! Macrophages M2 有组间显著性差异(p<0.05) [0m
## [31m 34 : Oh yeah! Mast cells 有组间显著性差异(p<0.05) [0m
## [32m 35 : Oopps! Megakaryocytes 没有组间显著性差异 [0m
## [32m 36 : Oopps! Melanocytes 没有组间显著性差异 [0m
## [32m 37 : Oopps! Memory B-cells 没有组间显著性差异 [0m
## [32m 38 : Oopps! Mesangial cells 没有组间显著性差异 [0m
## [31m 39 : Oh yeah! Monocytes 有组间显著性差异(p<0.05) [0m
## [32m 40 : Oopps! Myocytes 没有组间显著性差异 [0m
## [31m 41 : Oh yeah! NK cells 有组间显著性差异(p<0.001) [0m
## [31m 42 : Oh yeah! NKT 有组间显著性差异(p<0.01) [0m
## [31m 43 : Oh yeah! Neurons 有组间显著性差异(p<0.01) [0m
## [32m 44 : Oopps! Neutrophils 没有组间显著性差异 [0m
## [32m 45 : Oopps! Osteoblast 没有组间显著性差异 [0m
## [32m 46 : Oopps! Pericytes 没有组间显著性差异 [0m
## [32m 47 : Oopps! Plasma cells 没有组间显著性差异 [0m
## [32m 48 : Oopps! Platelets 没有组间显著性差异 [0m
## [32m 49 : Oopps! Preadipocytes 没有组间显著性差异 [0m
## [32m 50 : Oopps! Sebocytes 没有组间显著性差异 [0m
## [32m 51 : Oopps! Skeletal muscle 没有组间显著性差异 [0m
## [32m 52 : Oopps! Smooth muscle 没有组间显著性差异 [0m
## [32m 53 : Oopps! Tgd cells 没有组间显著性差异 [0m
## [31m 54 : Oh yeah! Th1 cells 有组间显著性差异(p<0.05) [0m
## [31m 55 : Oh yeah! Th2 cells 有组间显著性差异(p<0.05) [0m
## [32m 56 : Oopps! Tregs 没有组间显著性差异 [0m
## [31m 57 : Oh yeah! aDC 有组间显著性差异(p<0.01) [0m
## [32m 58 : Oopps! cDC 没有组间显著性差异 [0m
## [32m 59 : Oopps! iDC 没有组间显著性差异 [0m
## [32m 60 : Oopps! ly Endothelial cells 没有组间显著性差异 [0m
## [32m 61 : Oopps! mv Endothelial cells 没有组间显著性差异 [0m
## [32m 62 : Oopps! naive B-cells 没有组间显著性差异 [0m
## [32m 63 : Oopps! pDC 没有组间显著性差异 [0m
## [32m 64 : Oopps! pro B-cells 没有组间显著性差异 [0m## Warning: Vectorized input to `element_text()` is not officially supported.
## ℹ Results may be unexpected or may change in future versions of ggplot2.## Time difference of 15.16357 secsresult_imm01_1<-immu_visual(im=result_imm01$imm_data[,c(1,67,68,69)],exp=NULL,#
method = 'xCell',
sample_group=Gene_group_CRC1,
tumor=" ",heatmap=TRUE,
Type=c("Wild", "Mut"),
color=c("#757575", "#FF4040"),
geom_text=TRUE,
test = "wilcox.test")## # A tibble: 1,846 × 9
## # Groups: method, Group [6]
## SAMPLE_ID Group method method_score Q1 Q3 IQR LowerLimit UpperLimit
## <chr> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TCGA-3L-AA… Wild Immun… 1.03 0.304 1.32 1.02 -1.22 2.85
## 2 TCGA-3L-AA… Wild Strom… 0.517 0.101 0.479 0.378 -0.465 1.05
## 3 TCGA-3L-AA… Wild Micro… 1.55 0.514 1.71 1.20 -1.28 3.50
## 4 TCGA-4N-A9… Wild Immun… 0.725 0.304 1.32 1.02 -1.22 2.85
## 5 TCGA-4N-A9… Wild Strom… 0.0237 0.101 0.479 0.378 -0.465 1.05
## 6 TCGA-4N-A9… Wild Micro… 0.749 0.514 1.71 1.20 -1.28 3.50
## 7 TCGA-4T-AA… Wild Immun… 0.371 0.304 1.32 1.02 -1.22 2.85
## 8 TCGA-4T-AA… Wild Strom… 0.00474 0.101 0.479 0.378 -0.465 1.05
## 9 TCGA-4T-AA… Wild Micro… 0.376 0.514 1.71 1.20 -1.28 3.50
## 10 TCGA-5M-AA… Wild Immun… 0.0676 0.304 1.32 1.02 -1.22 2.85
## # ℹ 1,836 more rows
## [31m 1 : Oh yeah! ImmuneScore 有组间显著性差异(p<0.01) [0m
## [32m 2 : Oopps! StromaScore 没有组间显著性差异 [0m
## [32m 3 : Oopps! MicroenvironmentScore 没有组间显著性差异 [0m## Warning: Vectorized input to `element_text()` is not officially supported.
## ℹ Results may be unexpected or may change in future versions of ggplot2.## Time difference of 0.573478 secsresult_imm01$imm_plot+
theme(legend.position = "right")+
annotation_custom(
grob = ggplotGrob(result_imm01_1$imm_plot+
theme(legend.position = "none",
axis.title.y=element_blank(),
axis.text.x = element_text(face = "plain", angle = 30,
size=8,
hjust = 1))
),
xmin = 30.5,
xmax = 53.5,
ymin = 2.0,
ymax = 3.5
) 
5.2.2.2 limma.dif.visual
This function visualizes the Differential Gene Expression Data.
## Loading required package: S4Vectors## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: generics##
## Attaching package: 'generics'## The following objects are masked from 'package:igraph':
##
## components, union## The following object is masked from 'package:dplyr':
##
## explain## The following objects are masked from 'package:base':
##
## as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
## setequal, union##
## Attaching package: 'BiocGenerics'## The following object is masked from 'package:limma':
##
## plotMA## The following objects are masked from 'package:ggcor':
##
## ncols, nrows## The following objects are masked from 'package:igraph':
##
## normalize, path## The following object is masked from 'package:dplyr':
##
## combine## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
## as.data.frame, basename, cbind, colnames, dirname, do.call,
## duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
## mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
## unsplit, which.max, which.min##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:clusterProfiler':
##
## rename## The following object is masked from 'package:tidyr':
##
## expand## The following objects are masked from 'package:dplyr':
##
## first, rename## The following object is masked from 'package:utils':
##
## findMatches## The following objects are masked from 'package:base':
##
## I, expand.grid, unname## Loading required package: IRanges##
## Attaching package: 'IRanges'## The following object is masked from 'package:clusterProfiler':
##
## slice## The following objects are masked from 'package:dplyr':
##
## collapse, desc, slice## The following object is masked from 'package:grDevices':
##
## windows## Loading required package: GenomicRanges## Loading required package: GenomeInfoDb## Warning: package 'GenomeInfoDb' was built under R version 4.5.1## Loading required package: SummarizedExperiment## Loading required package: MatrixGenerics## Loading required package: matrixStats##
## Attaching package: 'matrixStats'## The following object is masked from 'package:dplyr':
##
## count##
## Attaching package: 'MatrixGenerics'## The following objects are masked from 'package:matrixStats':
##
## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.##
## Attaching package: 'Biobase'## The following object is masked from 'package:MatrixGenerics':
##
## rowMedians## The following objects are masked from 'package:matrixStats':
##
## anyMissing, rowMedians## The following object is masked from 'package:Hmisc':
##
## contents## Warning: package 'edgeR' was built under R version 4.5.1## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ purrr 1.1.0.9000
## ✔ lubridate 1.9.4 ✔ readr 2.1.5## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ lubridate::%--%() masks igraph::%--%()
## ✖ lubridate::%within%() masks IRanges::%within%()
## ✖ BiocGenerics::Position() masks ggplot2::Position(), base::Position()
## ✖ tibble::as_data_frame() masks igraph::as_data_frame(), dplyr::as_data_frame()
## ✖ IRanges::collapse() masks dplyr::collapse()
## ✖ Biobase::combine() masks BiocGenerics::combine(), dplyr::combine()
## ✖ purrr::compose() masks igraph::compose()
## ✖ matrixStats::count() masks dplyr::count()
## ✖ tidyr::crossing() masks igraph::crossing()
## ✖ IRanges::desc() masks dplyr::desc()
## ✖ S4Vectors::expand() masks tidyr::expand()
## ✖ clusterProfiler::filter() masks ggcor::filter(), dplyr::filter(), stats::filter()
## ✖ S4Vectors::first() masks dplyr::first()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()
## ✖ S4Vectors::rename() masks clusterProfiler::rename(), dplyr::rename()
## ✖ lubridate::second() masks S4Vectors::second()
## ✖ lubridate::second<-() masks S4Vectors::second<-()
## ✖ purrr::simplify() masks clusterProfiler::simplify(), igraph::simplify()
## ✖ IRanges::slice() masks clusterProfiler::slice(), dplyr::slice()
## ✖ Hmisc::src() masks dplyr::src()
## ✖ Hmisc::summarize() masks dplyr::summarize()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors## Loading required package: AnnotationDbi
##
## Attaching package: 'AnnotationDbi'
##
## The following object is masked from 'package:clusterProfiler':
##
## select
##
## The following object is masked from 'package:dplyr':
##
## select## Warning: package 'enrichplot' was built under R version 4.5.1## enrichplot v1.28.4 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
##
## Please cite:
##
## T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan,
## X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal
## enrichment tool for interpreting omics data. The Innovation. 2021,
## 2(3):100141
##
## Attaching package: 'enrichplot'
##
## The following object is masked from 'package:ggpubr':
##
## color_palettelibrary(base)
exp_CRC<-exp_raw$data
result<-limma.dif.visual(exprdata=exp_CRC[,-1],
pdata=Gene_group_CRC1,datatype="TPM",
Type=c("Wild", "Mut"),diff_method="limma",
contrastfml="Wild - Mut",
tumor="CRC TCGA",
P.Value=0.05,
logFC=0.5,tidyHeatmap=TRUE,
color= NULL,
ann_colors = list(regulate = c(Down = "#1B9E77", Up = "#D95F02"),
PREX2 = c(Wild = "#757575", Mut = "#FF4040")),
Regulate=c("Up","Down"),GO=TRUE,GO.plot="dotplot",split=TRUE,
KEGG=TRUE,KEGG.plot="dotplot",rel_heights= c(1.5, 0.5, 1))## 'data.frame': 35487 obs. of 8 variables:
## $ logFC : num -0.919 -1.096 -1.065 -1.086 -0.826 ...
## $ AveExpr : num -2.94e-01 -1.03e-01 -4.10e-01 -1.76e-06 -4.47e-01 ...
## $ t : num -8.57 -8.05 -7.78 -7.7 -7.54 ...
## $ P.Value : num 8.04e-17 4.11e-15 2.98e-14 5.21e-14 1.60e-13 ...
## $ adj.P.Val: num 2.87e-12 7.35e-11 3.55e-10 4.65e-10 1.14e-09 ...
## $ B : num 27.2 23.5 21.6 21.1 20 ...
## $ regulate : chr "Down (1491 genes)" "Down (1491 genes)" "Down (1491 genes)" "Down (1491 genes)" ...
## $ gene : chr "DLGAP1-AS5" "HMSD" "TRDV1" "MBP" ...
## variables idd target_group value condiction
## 1 A1CF TCGA-AA-3692-01A Wild -0.0368 Up
## 2 A1CF TCGA-WS-AB45-01A Wild -0.3920 Up
## 3 A1CF TCGA-AA-3561-01A Wild 0.2775 Up
## 4 A1CF TCGA-D5-6930-01A Wild -0.5037 Up
## 5 A1CF TCGA-AG-3582-01A Wild 0.0393 Up
## 6 A1CF TCGA-AA-3516-01A Wild -1.3484 Up## 'select()' returned 1:1 mapping between keys and columns
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
##
## preparing geneSet collections...
## GSEA analysis...## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some pathways, in reality P-values are less than 1e-10. You can
## set the `eps` argument to zero for better estimation.## leading edge analysis...
## done...
## Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...
## Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...
## using 'fgsea' for GSEA analysis, please cite Korotkevich et al (2019).
##
## preparing geneSet collections...
## GSEA analysis...
## leading edge analysis...
## done...
5.2.2.3 gene.mut_exp
This function visualizes the Gene Expression Difference with gene mutated status.
#example
#gene.mut_exp(mutation_data=mutation_CRC,exp=exp_CRC[,-1],colnum=2,gene=NULL,top=10,visual=TRUE,
# test_type= "parametric",title= "CRC TCGA",test="wilcox.test",only_red=TRUE,gene_vaf=F,
# color_0.05= "#FF34B3",color_0.01="#9400D3",color_0.001="#CD3700",color_0.0001="red",bar=T)
5.3 other
5.3.1 HRDscore
Determining genomic scar score (telomeric allelic imbalance, loss-off heterozigosity, large-scle transitions), signs of homologous recombination deficiency
#example
root_dir <-system.file("HRD", package = "Mypackage")
data("metadata")
HRD<-HRDscore(dirpath=root_dir,file_id_map=metadata,ploidy=NULL,reference="grch38")## Warning: package 'data.table' was built under R version 4.5.1##
## Attaching package: 'data.table'## The following objects are masked from 'package:lubridate':
##
## hour, isoweek, mday, minute, month, quarter, second, wday, week,
## yday, year## The following object is masked from 'package:purrr':
##
## transpose## The following object is masked from 'package:SummarizedExperiment':
##
## shift## The following object is masked from 'package:GenomicRanges':
##
## shift## The following object is masked from 'package:IRanges':
##
## shift## The following objects are masked from 'package:S4Vectors':
##
## first, second## The following objects are masked from 'package:dplyr':
##
## between, first, last## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI
## Determining HRD-LOH, LST, TAI## SampleID LOH TAI LST HRDsum HRD
## 1 TCGA-24-1930 7 21 23 51 HRD+
## 2 TCGA-25-1321 14 20 20 54 HRD+
## 3 TCGA-04-1525 3 19 7 29 HRD-
## 4 TCGA-23-1122 5 33 28 66 HRD+
## 5 TCGA-09-1667 7 26 11 44 HRD+
## 6 TCGA-61-1895 21 25 34 80 HRD+5.3.2 sig_Heatmap
heatmap() takes a tbl object and easily produces a ComplexHeatmap plot, with integration with tibble and dplyr frameworks.
#example
data("input1")
data("condiction")
feas1<-colnames(input1)[3:102]
sig_Heatmap(input = input1, features = feas1,ID ="SAMPLE_ID",show_plot=F,
condiction=condiction,id_condiction=colnames(condiction)[[1]],col_condiction=colnames(condiction)[[2]],
cols_group=c("#757575","#FF4040"),row_group=c("red","green"),
legend_show=TRUE,column_title_size=10,row_title_size=8,
heatmap_col=NULL,
#heatmap_col=c("#0505FA", "#FFFFFF", "#FA050D"),
group = "PREX2",row_title="Regulate", scale = TRUE,name="Expression")## variables idd target_group value condiction
## 1 ABCC5 TCGA-IN-A6RN-01 Wild 1.0065 Up
## 2 ABCC5 TCGA-BR-8687-01 Wild -0.3104 Up
## 3 ABCC5 TCGA-VQ-AA6I-01 Wild 0.2197 Up
## 4 ABCC5 TCGA-CD-A4MI-01 Mut -0.4678 Up
## 5 ABCC5 TCGA-IN-A6RL-01 Wild 2.2081 Up
## 6 ABCC5 TCGA-MX-A666-01 Wild 0.7078 Up
5.3.3 ggsurvplots
Drawing Survival Curves Using ggplot2
#example
data("clin_TCGA")
ggsurvplots(data = clin_TCGA, conf.int = FALSE,time_col = "PFS_MONTHS",
status_col = "PFS_STATUS", group_col = "Status", pvalue_table = TRUE,
palette = ggsci::pal_ucscgb()(4), risk.table = FALSE, title = NULL,
legend.labs = c("no KRAS or TP53", "TP53", "KRAS", "KRAS&TP53"),
xlab = "PFS_MONTHS", ylab = "Survival probability",
surv.median.line="hv",surv.scale="default",legend=FALSE)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:Biobase':
##
## combine## The following object is masked from 'package:BiocGenerics':
##
## combine## The following object is masked from 'package:dplyr':
##
## combine## [1] "#00FF00FF" "#FF9900FF" "#FF0000FF" "#FFCC00FF"## Warning in geom_segment(aes(x = 0, y = max(y2), xend = max(x1), yend = max(y2)), : All aesthetics have length 1, but the data has 4 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## All aesthetics have length 1, but the data has 4 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
5.3.4 cox_forest
Cox Proportional Hazards Univariate and Multivariate Forest Plot Generator
#example
data("aa")
cox = cox_forest(data=aa,
time_col = "PFI.time",
status_col = "PFI",
Univariate=T,
univar_predictors=colnames(aa)[c(5:7,18:22,34,31)],
Multivariate=T,
multivar_predictors = colnames(aa)[c(5:7,18:21,33,31)],
show_plots = T,xticks1=NULL,#c(0,0.25,0.5,0.75,1.00,1.25,1.5,6.5,11),
xticks2=NULL,#c(0,0.25,0.5,0.75,1.00,2,2.5,6,15),
title_univar = "PFI Univariate",
title_multivar = "PFI Multivariate",
use_baseline_table = TRUE,all=F,forestplot=F,
ci_pch=16,ci_col="darkred",ci_line="lightgreen",zero_col="#e22e2a",
log2=T,footnote=paste("\nHRD5: with HRD value adjusted(median)", "HRD adjusted = LST-15.5*ploidy+LOH+TAI ",sep = "\n"))## Loading required package: forestplot## Warning: package 'forestplot' was built under R version 4.5.1## Loading required package: checkmate##
## Attaching package: 'checkmate'## The following object is masked from 'package:Biobase':
##
## anyMissing## The following object is masked from 'package:matrixStats':
##
## anyMissing## Loading required package: abind## Loading required package: forestploter## Loading required package: tableone## Loading required package: plyr## ------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ------------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following object is masked from 'package:purrr':
##
## compact## The following object is masked from 'package:matrixStats':
##
## count## The following object is masked from 'package:IRanges':
##
## desc## The following object is masked from 'package:S4Vectors':
##
## rename## The following objects are masked from 'package:clusterProfiler':
##
## arrange, mutate, rename, summarise## The following object is masked from 'package:ggpubr':
##
## mutate## The following object is masked from 'package:ggcor':
##
## mutate## The following objects are masked from 'package:Hmisc':
##
## is.discrete, summarize## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize##
## level Overall
## n 604
## GRADE (%) G1 6 ( 1.0)
## G2 78 (12.9)
## G3 503 (83.3)
## G4 1 ( 0.2)
## GB 2 ( 0.3)
## GX 10 ( 1.7)
## missing 4 ( 0.7)
## AGE (mean (SD)) 59.63 (11.47)
## AJCC_Stage (%) I 17 ( 2.8)
## II 33 ( 5.5)
## III 460 (76.2)
## IV 89 (14.7)
## missing 5 ( 0.8)
## TMB_NONSYNONYMOUS (mean (SD)) 1.63 (0.91)
## LOH (mean (SD)) 12.19 (10.39)
## TAI (mean (SD)) 22.37 (7.21)
## LST (mean (SD)) 23.50 (22.60)
## HRDsum (mean (SD)) 58.06 (35.07)
## HRD6 (%) BRCA+&HRD+ 11 ( 1.8)
## BRCA+&HRD- 12 ( 2.0)
## BRCA-&HRD+ 285 (47.2)
## BRCA-&HRD- 275 (45.5)
## missing 21 ( 3.5)
## adjusted_HRDsum (mean (SD)) 27.72 (44.41)## Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
## Loglik converged before variable 8,9,10 ; coefficient may be infinite.##
## level Overall
## n 604
## GRADE (%) G1 6 ( 1.0)
## G2 78 (12.9)
## G3 503 (83.3)
## G4 1 ( 0.2)
## GB 2 ( 0.3)
## GX 10 ( 1.7)
## missing 4 ( 0.7)
## AGE (mean (SD)) 59.63 (11.47)
## AJCC_Stage (%) I 17 ( 2.8)
## II 33 ( 5.5)
## III 460 (76.2)
## IV 89 (14.7)
## missing 5 ( 0.8)
## TMB_NONSYNONYMOUS (mean (SD)) 1.63 (0.91)
## LOH (mean (SD)) 12.19 (10.39)
## TAI (mean (SD)) 22.37 (7.21)
## LST (mean (SD)) 23.50 (22.60)
## HRD5 (%) Negative 275 (45.5)
## Positive 308 (51.0)
## missing 21 ( 3.5)
## adjusted_HRDsum (mean (SD)) 27.72 (44.41)
5.3.5 GSEAplot2
GSEA plot that mimic the plot generated by broad institute’s GSEA software
#example
GSEAplot2(result$GO,geneSetID=if(length(result$GO@result[["ID"]])>10) c(1:10) else c(1:c(1:length(result$GO@result[["ID"]]))),ES_geom = "line",legend.position ="none",pvalue_table=FALSE,
title = paste("GO enrichment"),rel_heights = c(4, 3, 2),
base_size = 20,Type=c("Wild", "Mut"))## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.## [31m immune system process (pvalue = 0,NES = -3.052512) [0m
## [31m immune response (pvalue = 0,NES = -2.953593) [0m
## [31m defense response to symbiont (pvalue = 0,NES = -2.902195) [0m
## [31m defense response to other organism (pvalue = 0,NES = -2.802582) [0m
## [31m response to external biotic stimulus (pvalue = 0,NES = -2.79815) [0m
## [31m response to other organism (pvalue = 0,NES = -2.79815) [0m
## [31m response to biotic stimulus (pvalue = 0,NES = -2.765313) [0m
## [31m biological process involved in interspecies interaction between organisms (pvalue = 0,NES = -2.77826) [0m
## [31m innate immune response (pvalue = 1e-06,NES = -2.809501) [0m
## [31m regulation of immune response (pvalue = 1e-06,NES = -2.709404) [0m
6 References
- Yao J, Sun Q, Wu H, et al. Decoding the molecular landscape: HER2 and PD-L1 in advanced gastric cancer. Front Immunol. 2025;16:1567308. doi:10.3389/fimmu.2025.1567308
- Qiu Q, Tan D, Chen Q, et al. Clinical implications of PD-L1 expression and pathway-related molecular subtypes in advanced Asian colorectal cancer patients. Am J Cancer Res. 2024;14(2):796-808. doi:10.62347/FSSF9938
- Ding W, Yang P, Zhao X, et al. Unraveling EGFR-TKI resistance in lung cancer with high PD-L1 or TMB in EGFR-sensitive mutations. Respir Res. 2024;25(1):40. doi:10.1186/s12931-023-02656-3
- Peng H, Ying J, Zang J, et al. Specific Mutations in APC, with Prognostic Implications in Metastatic Colorectal Cancer. Cancer Res Treat. 2023;55(4):1270-1280. doi:10.4143/crt.2023.415
- Jiang Y, Mai G, Zhao X, et al. Molecular characterization and prognostic implications of KRAS mutations in pancreatic cancer patients: insights from multi-cohort analysis. NPJ Precis Oncol. 2025;9(1):299. Published 2025 Aug 22. doi:10.1038/s41698-025-01087-1
- Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487(7407):330-337. Published 2012 Jul 18. doi:10.1038/nature11252